{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9.6.2_Support Vector Machine "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T12:17:32.762785Z",
     "start_time": "2023-09-19T12:17:32.661911Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import train_test_split,GridSearchCV\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix,roc_auc_score,roc_curve\n",
    "\n",
    "from mlxtend.plotting import plot_decision_regions\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating toy dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T12:17:32.772178Z",
     "start_time": "2023-09-19T12:17:32.766893Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "         X1        X2  y\n0  0.263510  4.550452  0\n1  2.564651  0.372574  0\n2  3.869189  2.282377  0\n3  0.135411  2.458516  0\n4  2.109994  1.045496  0",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>X1</th>\n      <th>X2</th>\n      <th>y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.263510</td>\n      <td>4.550452</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2.564651</td>\n      <td>0.372574</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3.869189</td>\n      <td>2.282377</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.135411</td>\n      <td>2.458516</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2.109994</td>\n      <td>1.045496</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#setting the seed \n",
    "X = np.random.normal(size = (200,2))\n",
    "X[:100,] += 2\n",
    "X[100:150,] -= 2\n",
    "y = [0]*150 + [1]*50\n",
    "\n",
    "data = pd.DataFrame({'X1':X[:,0],'X2':X[:,1],'y':y})\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T12:17:33.137202Z",
     "start_time": "2023-09-19T12:17:32.773980Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: xlabel='X1', ylabel='X2'>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x600 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the data \n",
    "plt.figure(figsize = (10,6))\n",
    "sns.scatterplot(x = 'X1',y = 'X2',hue = 'y',data = data,s = 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can say that there is no possible linearly separation between the two classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T12:17:33.138310Z",
     "start_time": "2023-09-19T12:17:33.045016Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 2) (100, 2)\n"
     ]
    }
   ],
   "source": [
    "#splitting the data into train and test\n",
    "X_train,X_test,y_train,y_test = train_test_split(data.drop('y',axis=1),data['y'],test_size = 0.5,random_state = 1)\n",
    "print(X_train.shape,X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitting the model with radical kernal to training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T12:17:33.223928Z",
     "start_time": "2023-09-19T12:17:33.049416Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "SVC(C=1, gamma=1)",
      "text/html": "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=1, gamma=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=1, gamma=1)</pre></div></div></div></div></div>"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm = SVC(kernel='rbf',gamma = 1,C = 1)\n",
    "svm.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the decision boundary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T12:17:34.108561Z",
     "start_time": "2023-09-19T12:17:33.064544Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/py309/lib/python3.9/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but SVC was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": "Text(0, 0.5, 'X2')"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x600 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,6))\n",
    "plot_decision_regions(np.array(X_train), np.array(y_train), clf=svm, legend=2)\n",
    "\n",
    "plt.xlabel('X1')\n",
    "plt.ylabel('X2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T12:17:34.116380Z",
     "start_time": "2023-09-19T12:17:34.110853Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Support Vectors are  51\n",
      "Accuracy on training data is  0.9\n"
     ]
    }
   ],
   "source": [
    "print('Number of Support Vectors are ',svm.support_vectors_.shape[0])\n",
    "print('Accuracy on training data is ',svm.score(X_train,y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Choosing a high number of C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T12:17:34.860492Z",
     "start_time": "2023-09-19T12:17:34.118446Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/py309/lib/python3.9/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but SVC was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Support Vectors are  37\n",
      "Accuracy on training data is  1.0\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x600 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "svm = SVC(kernel='rbf',gamma = 1,C = 10e5)\n",
    "svm.fit(X_train,y_train)\n",
    "\n",
    "### Plotting the decision boundary\n",
    "\n",
    "plt.figure(figsize = (10,6))\n",
    "plot_decision_regions(np.array(X_train), np.array(y_train), clf=svm, legend=2)\n",
    "\n",
    "plt.xlabel('X1')\n",
    "plt.ylabel('X2')\n",
    "\n",
    "print('Number of Support Vectors are ',svm.support_vectors_.shape[0])\n",
    "print('Accuracy on training data is ',svm.score(X_train,y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Altough we are having a better training accuracy this time,but we can see that the model is overfitting the data, and it will perform poor on test data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Choosing the optimal value of C and gamma using cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T12:17:35.223755Z",
     "start_time": "2023-09-19T12:17:34.859108Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "GridSearchCV(cv=10, estimator=SVC(),\n             param_grid={'C': [0.1, 1, 10, 100, 1000],\n                         'gamma': [0.5, 1, 2, 3, 4]})",
      "text/html": "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=SVC(),\n             param_grid={&#x27;C&#x27;: [0.1, 1, 10, 100, 1000],\n                         &#x27;gamma&#x27;: [0.5, 1, 2, 3, 4]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=SVC(),\n             param_grid={&#x27;C&#x27;: [0.1, 1, 10, 100, 1000],\n                         &#x27;gamma&#x27;: [0.5, 1, 2, 3, 4]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div></div></div></div></div></div>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params_dict = {'C':[0.1,1,10,100,1000],'gamma':[0.5,1,2,3,4]}\n",
    "search = GridSearchCV(SVC(kernel = 'rbf'),params_dict,cv =10)\n",
    "search.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T12:17:35.231234Z",
     "start_time": "2023-09-19T12:17:35.224264Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "{'C': 1, 'gamma': 0.5}"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the values are different as compared to the one that we have in the book....and i hope that you know why it is happening that way..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T12:17:35.231387Z",
     "start_time": "2023-09-19T12:17:35.228070Z"
    }
   },
   "outputs": [],
   "source": [
    "best_model = search.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance of best_model on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T12:17:35.239816Z",
     "start_time": "2023-09-19T12:17:35.234303Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy is  0.84\n",
      "Confusion matrix is - \n",
      "[[67  3]\n",
      " [13 17]]\n"
     ]
    }
   ],
   "source": [
    "test_preds = best_model.predict(X_test)\n",
    "print('Test accuracy is ',accuracy_score(y_test,test_preds))\n",
    "print('Confusion matrix is - ')\n",
    "print(confusion_matrix(y_test,test_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9.6.3 ROC curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T12:17:35.248199Z",
     "start_time": "2023-09-19T12:17:35.239968Z"
    }
   },
   "outputs": [],
   "source": [
    "def plot_roc(cls,X,y,title):\n",
    "    probs = cls.predict_proba(X)[:,1]\n",
    "    fpr, tpr, _ = roc_curve(y,probs)\n",
    "    auc = roc_auc_score(y,probs)\n",
    "    \n",
    "    plt.figure(figsize = (6,6))\n",
    "    plt.plot(fpr,tpr,label=\"auc=\"+str(auc)[0:4],c = 'r')\n",
    "    plt.plot([0,1],[0,1],alpha = 0.1,c = 'b')\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title(title)\n",
    "    plt.legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T12:17:35.271804Z",
     "start_time": "2023-09-19T12:17:35.244398Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "SVC(C=1, gamma=0.5, probability=True)",
      "text/html": "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=1, gamma=0.5, probability=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=1, gamma=0.5, probability=True)</pre></div></div></div></div></div>"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#because we need probability = True to get probs\n",
    "best_model.probability = True\n",
    "best_model.gamma = 0.5\n",
    "best_model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T12:17:35.397732Z",
     "start_time": "2023-09-19T12:17:35.251762Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 600x600 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc(best_model,X_train,y_train,'training roc, gamma = 0.5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T12:17:35.519266Z",
     "start_time": "2023-09-19T12:17:35.401941Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 600x600 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#roc curve if gamma is high\n",
    "best_model.probability = True\n",
    "best_model.gamma = 50\n",
    "best_model.fit(X_train,y_train)\n",
    "\n",
    "plot_roc(best_model,X_train,y_train,'training roc, gamma = 0.5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "woah...but wait, its training roc, not test roc :("
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the same on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T12:17:35.519560Z",
     "start_time": "2023-09-19T12:17:35.515443Z"
    }
   },
   "outputs": [],
   "source": [
    "def find_roc_terms(cls,X,y):\n",
    "    probs = cls.predict_proba(X)[:,1]\n",
    "    fpr,tpr,_ = roc_curve(y,probs)\n",
    "    auc = roc_auc_score(y,probs)\n",
    "    \n",
    "    return fpr,tpr,auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T12:17:35.529075Z",
     "start_time": "2023-09-19T12:17:35.522034Z"
    }
   },
   "outputs": [],
   "source": [
    "def plot_roc_test(cls,X_train,y_train,X_test,y_test):\n",
    "    fpr,tpr,auc = find_roc_terms(cls,X_train,y_train)\n",
    "    fpr_test,tpr_test,auc_test = find_roc_terms(cls,X_test,y_test)\n",
    "    \n",
    "    fig,(ax1,ax2) = plt.subplots(1,2,figsize = (12,6))\n",
    "    ax1.plot(fpr,tpr,label=\"auc=\"+str(auc)[0:4],c = 'r')\n",
    "    ax1.plot([0,1],[0,1],alpha = 0.1,c = 'b')\n",
    "    ax1.set_xlabel('False Positive Rate')\n",
    "    ax1.set_ylabel('True Positive Rate')\n",
    "    ax1.set_title('Train Roc')\n",
    "    ax1.legend()\n",
    "    \n",
    "    ax2.plot(fpr_test,tpr_test,label=\"auc=\"+str(auc_test)[0:4],c = 'r')\n",
    "    ax2.plot([0,1],[0,1],alpha = 0.1,c = 'b')\n",
    "    ax2.set_xlabel('False Positive Rate')\n",
    "    ax2.set_ylabel('True Positive Rate')\n",
    "    ax2.set_title('Test Roc')\n",
    "    ax2.legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T12:17:35.739612Z",
     "start_time": "2023-09-19T12:17:35.526197Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1200x600 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for gamma = 0.5 \n",
    "best_model.gamma = 0.5\n",
    "best_model.fit(X_train,y_train)\n",
    "\n",
    "plot_roc_test(best_model,X_train,y_train,X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T12:17:35.959987Z",
     "start_time": "2023-09-19T12:17:35.737748Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1200x600 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for gamma = 50\n",
    "best_model.gamma = 50\n",
    "best_model.fit(X_train,y_train)\n",
    "\n",
    "plot_roc_test(best_model,X_train,y_train,X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can infer from the last two graphs that model with gamma = 50, is highly overfitting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T12:17:35.966281Z",
     "start_time": "2023-09-19T12:17:35.957985Z"
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
